Using Genetics to Inform Interventions Related to Sodium and Potassium in Hypertension

BACKGROUND: Hypertension is a key risk factor for major adverse cardiovascular events but remains difficult to treat in many individuals. Dietary interventions are an effective approach to lower blood pressure (BP) but are not equally effective across all individuals. BP is heritable, and genetics may be a useful tool to overcome treatment response heterogeneity. We investigated whether the genetics of BP could be used to identify individuals with hypertension who may receive a particular benefit from lowering sodium intake and boosting potassium levels. METHODS: In this observational genetic study, we leveraged cross-sectional data from up to 296 475 genotyped individuals drawn from the UK Biobank cohort for whom BP and urinary electrolytes (sodium and potassium), biomarkers of sodium and potassium intake, were measured. Biologically directed genetic scores for BP were constructed specifically among pathways related to sodium and potassium biology (pharmagenic enrichment scores), as well as unannotated genome-wide scores (conventional polygenic scores). We then tested whether there was a gene-by-environment interaction between urinary electrolytes and these genetic scores on BP. RESULTS: Genetic risk and urinary electrolytes both independently correlated with BP. However, urinary sodium was associated with a larger BP increase among individuals with higher genetic risk in sodium- and potassium-related pathways than in those with comparatively lower genetic risk. For example, each SD in urinary sodium was associated with a 1.47–mm Hg increase in systolic BP for those in the top 10% of the distribution of genetic risk in sodium and potassium transport pathways versus a 0.97–mm Hg systolic BP increase in the lowest 10% (P=1.95×10−3). This interaction with urinary sodium remained when considering estimated glomerular filtration rate and indexing sodium to urinary creatinine. There was no strong evidence of an interaction between urinary sodium and a standard genome-wide polygenic score of BP. CONCLUSIONS: The data suggest that genetic risk in sodium and potassium pathways could be used in a precision medicine model to direct interventions more specifically in the management of hypertension. Intervention studies are warranted.


UK Biobank (UKBB) Quality Control
In the UKBB, 13,568,914 autosomal variants survived a series of quality control steps, including, imputation quality filtering (INFO > 0.8), minor allele frequency (MAF) > 1 × 10 −4 , call rate > 0.98, and filtration of strong deviations from the Hardy-Weinberg equilibrium.We performed analyses for this pilot study in the largest ancestral group in the UKBB to maximise power (White British), however, we acknowledge the need for follow up studies to assess the transferability of our findings to other ancestral populations to ensure equitable development and application of genetics research.

Polygenic score generation (PGS) and tuning
PGS in individual  sums the effect size of  variants from the GWAS on blood pressure ( $ ! ), multiplied by its allelic dosage under an additive model ( "!  {0, 1,2}, equation 1).The full set of  variants included in the score is selected by linkage disequilibrium clumping and thresholding (LD C+T), whereby SNPs are 'clumped' such that the retained SNPs are largely independent and 'thresholded' based on their association P value in the GWAS 58 .

Pharmagenic enrichment score (PES) generation and tuning
Variants annotated to sodium/potassium biology were then subjected to LD C+T, T  {0.005, 0.05, 0.5, 1}.As outlined in previous work, these thresholds in subsetted scores like PES are designed to capture differing levels of the polygenic signal whilst still retaining enough independent variants such that they are adequately informative of the biology of the pathway 22,[24][25] .After this process, PES are profiled in the same fashion as genome wide PGS, but M consists of only clumped variants within the gene-set of interest (equation 2).
Profiling and tuning in the HCS was identical to the PGS.

Gene-by-environment effect modelling
We firstly screened the best performing PGS and PES for SBP and DBP in a model that added an interaction term between the urinary electrolyte and the genetic score via specified in equation 3, which includes only a GxE term ( &×(  "  " ).
PES or PGS with a nominally significant (P < 0.05) GxE term with either urinary sodium or potassium were the carried forward for additional sensitivity analyses.As shown previously, spurious GxE effects can be detected when interaction terms between the environmental exposure of interest (urinary sodium or potassium) are not included with all covariates ( *×(  " +  " ), as well as interaction terms between the genetic term and all covariates ( &×*  "  " + ) 37 .To address this we, we constructed two additional models for GxE pairs with some evidence for non-additivity that controlled for gene-by-covariate (GxC) effects (equation 4), and both GxC and covariate-by-environment (CxE) effects (equation 5).
We also tested the effect of adjusting for urinary creatinine, as well as estimating interactions in the full cohort that covaries for medication status.The statistical significance of GxE effects are also particularly prone to inflation due to heteroskedasticity 39 , and as such, we re-estimated the standard errors as heteroskedasticity consistent (HC) standard errors, specifically leveraging the HC0 (White's Estimator) and HC3 methods via the sandwich R package v3.0-1 40,41 .Finally, we also considered whether the G term of interest (PGS or PES) was associated with differences in blood pressure variance, rather than just mean effects.Genetic correlates with the variance of quantitative traits have previously been shown to be enriched for factors that display detectable GxE effects 42,43 .We tested this by splitting the relevant score into quantiles followed by testing for significant differences in variance between these quantiles using Levene's test.Specifically, this involved the cohort being split into both quartiles and deciles for comparison of variance using Levene's test.A stringent normalisation approach was used for this testing of variance effects, in accordance with previous literature related to genetic correlates of variance 43 , whereby the residuals of a model that regressed age, age 2 , 20 principal components, assessment centre, and assessment month on blood pressure were winsorized at five standard deviations above or below the mean, followed by normalisation to have a mean of zero and unit variance.This model was constructed in males and females separately to remove mean and variance differences between sexes.

Exploring the effects of estimated glomerular filtration rate (eGFR) and indexing to urinary creatinine
We sought to investigate the influence of renal function and urinary dilution effects on our interaction results.Indexing to urinary creatinine is undertaken in some studies to attempt to account for interindividual variation in urinary dilution that could impact the interpretation of spot urinary biomarkers 59 .We re-estimated the GxE effect of the sodium/potassium transport PES with urinary creatinine:urinary sodium (UCr:UNa + ) in the full model that included both GxC and ExC effects.We also repeated the percentile analyses with UCr:UNa + as the exposure variable and assessed the correlation of the estimated effect sizes with that of raw urinary sodium.Two different equations were then used to estimate GFR in each participant in our UKBB study cohort.We decided to use serum cystatin C as the main input for the eGFR equations given we had already performed creatinine related analyses and to explore the effects of different biomarkers.The eGFR equations were implemented using the nephro v 1.3 R package.Specifically, these were the CKD-EPI equation for cystatin C proposed by Inker et al. and the age and sex weighted equation for cystatin C proposed by Stevens et al. 44,45 .In line with previous genetic studies using eGFR, we winsorized eGFR values below 15 ml/min/1.73m 2 or above 200 ml/min/1.73m 2-resulting in the exclusion of 41 participants 60 .
We re-estimated the per-percentile effects additionally adjusting for eGFR, performing these analyses for both equations separately.We also split the cohort into an eGFR above or below ml/min/1.73m 2 is somewhat arbitrary but is often used as a value to represent 'normal' kidney function.These effect sizes were formally compared for statistically significant difference using the Z test described above in equation 6.Finally, we also included eGFR as a continuous variable in the full GxE model (ExC and GxC included).

Transcriptomic correlates of the polygenic scores
We investigated the similarity between the gene expression signature (correlation with mRNA expression) associated with the SBP sodium/potassium transport PES and genome wide SBP PGS, respectively.The Genotype-Tissue Expression (GTEx) v8 post-mortem dataset was utilised for this purpose, with the use of these data approved through dbGaP (application ID = #1017132-1, project ID = #27869) with PES or PGS as the explanatory variable, respectively, was tested using linear regression.
We estimated this correlation across the entire blood transcriptome and specifically within the genes from the sodium/potassium transport pathway used to construct the PES that were detectable in this dataset.The mean difference in these mRNA signatures between the scores was then tested using a paired t test.

Association of urinary sodium per decile of the sodium/potassium transport PES
We formally compared the urinary sodium effect sizes on SBP in each decile of the PES individually relative to the highest decile and found the hypertensive effect of sodium in the highest decile was statistically significantly larger (P < 0.05) relative to the first, second and fifth decile, with a trend (P < 0.1) for the third and seventh decile (Table S9).

Differential variance between quantiles of the PES
There was also evidence that the variance of SBP was significantly different between quantiles of the PES, thus adding support to the existence of non-additive effects given variance related genetic effects on quantitative traits are enriched for detectable GxE 42,43 .This phenomenon of unequal SBP variance was more pronounced between quartiles (P = 5.67 x 10 -3 , Levene's test), than deciles (P = 0.049, Levene's test) of the transport PES.

Exploring the effect of eGFR
Firstly, we re-performed the percentile analyses whereby the effect of urinary sodium on SBP was estimated in each percentile of the sodium/potassium transport PES.In this instance, we additionally added a covariate of eGFR (Stevens et al. and CKD-Epi equations both evaluated in separate models).Analogous to the eGFR unadjusted results, we found a significant association between increasing PES percentile and the urinary sodium effect size that was not seen for genome-wide PGS.The effect sizes between the unadjusted and adjusted analyses GxE estimates that were not statistically different from each other as compared using the Z test outlined in equation 6 (P = 0.622).The magnitude of the GxE effect was larger in those with eGFR < 90 ml/min/1.73m 2 , however, considering the associated standard error this in fact was not significantly different than those ≥ 90 ml/min/1.73m 2 .The PES was then split into deciles in each of these two partitions to estimate the per-decile urinary sodium effect size.We found a very similar pattern of a generally increasing urinary sodium effect size at elevated PES (Figure S10).We do note that the GxE effect was in either eGFR subset less precise than in the full cohort, which is likely a product of a reduced sample through partitioning by eGFR.
However, this does reinforce the need for larger samples in future work such that these effects can be investigated with greater fidelity.

Benchmarking to urinary creatinine
We found that upon benchmarking urinary sodium to urinary creatinine (UNa + :UCr) that the GxE effect with the sodium/potassium transport PES remained statistically significant, although was marginally attenuated relative to the raw urinary sodium estimate.Upon using sandwich estimators (HC) to account for heteroskedasticity, the standard error increases from approximately 0.044 to 0.0516, and thus, decreasing statistical significance to a trend (P = 0.08).We sought to investigate this further by repeating the PES percentile modelling with UNa + :UCr.There was moderate concordance between the per-percentile effect sizes of UNa + :UCr vs UNa + on SBP ( = 0.324, P = 1.01 × 10 -3 ).As visualised in figure S11, there is still clear evidence that the association of UNa + :UCr is smaller for individuals with low PES < 20 th percentile.However, there is much more heterogeneity above that threshold, with no consistent evidence of a difference between moderate (20 th to 80 th percentile) and high PES (> 80 th percentile).This can also be seen using the smoothed curves (LOESS, Figure S11) -whilst the attenuated association with SBP in the low PES group is relatively consistent for both UNa + :UCr and UNa + , the increasing SBP effect size beyond that is comparatively more linear with raw UNa + .This perhaps is not surprising in the sense that UNa + :UCr is a less interpretable phenotype than raw UNa + in the context of genetics as it assumes a purely linear relationship between UNa + and UCr -which are two heritable traits for which genetic variants plausibly will act differently on -as well as inducing effects of creatinine unrelated to renal function like body and muscle mass.In summary, we still see evidence of a gene-by-environment effect using UNa + :UCr, particularly for individuals with low PES; however, it is weakened compared to raw UNa + .Given the limitations of benchmarking to creatinine, future work should be focused on testing this interaction with 24-hour urine.For instance, as visualised in Figure S12, there is evidence of a non-linear relationship between urinary creatinine and urinary sodium at higher creatinine values > 1 SD above the mean.The data presented herein does at least suggest in the interim that the non-additive interplay between the PES and sodium is not purely an artefact of factors such as urinary dilution.

SUPPLEMENTARY FIGURES
Figure S1.Sources of variation in blood pressure and urinary electrolytes in the UK Biobank study cohort.Variance explained (R 2 ) by each of the variables on the y-axis for systolic blood pressure (SBP), diastolic blood pressure (DBP), urinary sodium (urinary Na + ), and urinary potassium (urinary K + ) from separate linear models.The outcomes on the left are untransformed, whilst the righthand plots represent results from models wherein the outcome was natural log transformed.

Figure S2. Relationship between increasing urinary sodium/potassium ratio and blood
pressure.Systolic blood pressure (SBP, left) and diastolic blood pressure (DBP, right) is plotted for categories for spot urinary sodium potassium ratio as a box-and-whisker plot overlaid on a violin plot.The x-axis denotes a sodium/potassium ratio < 1, between 1 and 2, between 2-3, between 3-4, and greater than 5.         Finally, we also fit a generalised additive model using thin plate regression splines (third panel) -the variance explained was similar to that of the glm with the quadratic term but with a lower BIC, BIC = 2325672.In general, we see evidence of a linear relationship of urinary creatinine to urinary sodium up until values > 1 SD above the mean, with deviations from linearity thereafter.The bottom row panel visualises this relationship in a simplified form whereby urinary creatinine is split into percentiles and the mean (+/-1 SD) is plotted (thin plate regression spline curve).This reinforces that the linearity of the urinary sodium ~ urinary correlation does start to dimmish at higher urinary creatinine values, although not with large magnitude through this method of analysis.An important consideration here is that this only summarises the association per-percentile and does oversimplify the relationship for ease of visualisation.and estimated potassium intake from the dietary questionnaire (bottom right).The bottom left is RFS distribution sodium/potassium ratio < 1, between 1 and 2, between 2-3, between 3-4, and greater than 5.

Figure S3 .
Figure S3.Tuning blood pressure polygenic scores (PGS) in the Hunter Community Study (HCS) cohort.Tuning genome-wide polygenic scores for SBP and DBP amongst medicated (antihypertensives) and unmedicated participants with measured blood pressure in the HCS.The variance explained between the full and covariate only model (Δ 6 ) for each P value threshold is plotted.

Figure S4 .
Figure S4.Association between a systolic blood pressure (SBP) polygenic score (PGS) and measured SBP.Violin plots, with overlaid box-and-whisker plots, of the distribution of measured SBP in each quintile of the SBP PGS in the UKBB cohort.The red dotted line denotes mean SBP in the entire cohort.

Figure S5 .
Figure S5.Effect sizes of sodium/potassium pharmagenic enrichment scores in the UKBB.Forest plot depicting the effect size (beta estimate with 95% confidence interval error bars) of the tuned PES in the UKBB on SBP and DBP, respectively.The top panel denotes models where genome wide PGS is covaried for, whilst the bottom panel are PGS unadjusted estimates.

Figure S6 .
Figure S6.The estimated effect size of urinary sodium on SBP at differing values of sodium/potassium transport PES and genome-wide PGS.The estimated effect urinary sodium on SBP in the cohort upon splitting participants into deciles of the Na/K transport PES (left) or the genome wide PGS (right), with error bars denoting 95% confidence intervals of the estimate.These blood pressure effect sizes (in mmHg) are per standard deviation(standardised to be one in each decile) for urinary sodium.The dotted line denotes the mean urinary sodium/SBP effect size over all the deciles for either PES or PRS.A linear trend line is plotted between the per-decile beta estimates to visualise the trend of the effect sizes.

Figure S7 .
Figure S7.Exploring the association of urinary potassium with diastolic blood pressure with increasing sodium/potassium renal excretion pharmagenic enrichment score.On the left, the DBP effect size (mmHg) per standard deviation (SD) of scaled urinary potassium (SD = 1) in each percentile of the sodium/potassium renal excretion pharmagenic enrichment score (PES).The per-percentile estimates were grouped into low (1 st -20 th percentile), moderate (20 th -80 th percentile), and high (80 th -top percentile).Box whisker plots were overlaid on the effect sizes in each group, denoting the median [+/-interquartile range) urinary sodium effect size for each group.The dotted line represents the mean SBP effect size across all percentiles.The righthand plot is analogous to the lefthand side but the percentiles instead represent percentiles of genome wide DBP polygenic score (PGS).

Figure S8 .Figure S9 .
Figure S8.Transcriptional correlates of PES and PGS.The sodium/potassium transport PES and the genome wide PGS were regressed on the whole blood transcriptome.The correlation between the regression t value (beta/SE) for each gene is plotted transcriptomewide (a) and specifically within the sodium/potassium transport gene-set (b).A kernel density estimation plot of the distribution of the absolute value of these regression t values is shown transcriptome-wide (a) and specifically within the sodium/potassium transport gene-set (b).

Figure S10 .
Figure S10.Estimated effect of urinary sodium per decile of the sodium/potassium transport PES -subsetted by eGFR (Stevens et al. eGFRCys).The estimated effect urinary sodium on SBP in the cohort upon splitting participants into deciles of the Na/K transport PES with error bars denoting 95% confidence intervals of the estimate.These blood pressure effect sizes (in mmHg) are per standard deviation (standardised to be one in each decile) for urinary sodium.The dotted line denotes the mean urinary sodium/SBP effect size over all the deciles for either PES or PRS.A linear trend line is plotted between the per-decile beta estimates to visualise the trend of the effect sizes.The left plot is indicative of these analyses performed in participants with an eGFR ≥ 90 ml/min/1.73m 2 , whilst the righthand plot relates to participants with eGFR < 90 ml/min/1.73m 2 .

Figure S11 .
Figure S11.Consistency between PES percentile estimates of urinary sodium on systolicblood pressure versus urinary sodium-to-urinary creatinine ratio.The top left hand-plot denotes the concordance between urinary sodium (UNa + ) versus urinary sodium-to-creatinine ratio (UNa + :UCr) effect sizes per PES percentile.The top right-hand plot represents the SBP effect size (mmHg) per standard deviation (SD) of scaled UNa + :UCr (SD = 1) in each percentile of the sodium/potassium transport pharmagenic enrichment score (PES), additionally adjusted for eGFR.The per-percentile estimates were grouped into low (1st-20th percentile), moderate (20th-80th percentile), and high (80th -top percentile).Box whisker plots were overlaid on the effect sizes in each group, denoting the median [+/-interquartile range) urinary sodium effect size for each group.The dotted line represents the mean SBP effect size across all percentiles.The bottom plots represent a smoothed LOESS curve between the per-PES-percentile effect size of UNa + (left) and UNa + :UCr (right).

Figure S12 .
Figure S12.Exploring linear and non-linear features of the relationship between urinary creatinine and urinary sodium (unmedicated individuals).Creatinine outliers > 5 standard deviations above the mean winsorized for visualisation purposes.Red-dotted line denotes the mean urinary creatinine value, whilst the blue dotted line denotes values > 1 standard deviation above the mean.In the first model, we fit a conventional generalised linear model (glm) -with this smoothed curve shown in blue on the plot.Adjusted for age, age 2 , sex, assessment centre, and assessment month the variance explained by a glm had Bayesian Information Criterion (BIC) of 2356578 .The second panel adds a quadratic which lowered the BIC, BIC = 2327628.

Figure S13 .
Figure S13.Ridgeline plot of the distribution (Kernel density estimation) of the recommended food score (RFS) amongst quantiles of outcome variables.The RFS distribution is plotted per quintile of urinary sodium (top left), urinary potassium (top right),